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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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8 results for “hypersonic reentry”

Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)stat.ML

This paper speeds up simulations of hypersonic reentry by combining traditional and neural methods.

problem Accurately simulating hypersonic reentry with chemical reactions is computationally expensive.

method A hybrid simulation code combining a traditional fluid dynamics solver with a neural network approximating chemical reactions.

result Achieved significant acceleration factors (

imes 10

imes 18.6

) while maintaining accuracy.

hypersonic reentry chemical reactions neural networks simulation acceleration accuracy guarantees

2022-09-27abs ↗pdf ↗

Continuous-Time Homeostatic Dynamics for Reentrant Inference Modelsmath.DS

FHRN uses continuous-time dynamics to stabilize reentrant neural computation.

problem Stabilizing reentrant neural computation.

method Formulated as a continuous-time neural-ODE system, revealing norm-regulated reentry.

result Achieves stable oscillatory trajectories through population-level gain modulation.

neural networks reentrant dynamics homeostasis continuous-time stability

2025-12-04abs ↗pdf ↗

A fully GPU-based workflow for building physics emulators of hypersonic flowscs.LG

A GPU-based workflow for building physics emulators of hypersonic flows

problem Resolving complex physical phenomena in hypersonic flows

method Fully GPU-based workflow integrating accelerated data generation and neural emulators

result Physics emulators remain reliable beyond their training distribution

GPU hypersonic flows physics emulators residual-based refinement differentiable simulation

2026-06-11abs ↗pdf ↗

Gradient-enhanced deep Gaussian processes for multifidelity modellingstat.ML

Gradient-enhanced deep GPs improve multifidelity model accuracy.

problem Improving accuracy in multifidelity models using gradient data.

method Extending deep Gaussian processes to incorporate gradient data.

result Gradient-enhanced deep GP outperforms other models in predicting aerodynamic coefficients.

multifidelity modelling deep Gaussian processes gradient-enhanced aerodynamic coefficients hypersonic flight

2024-02-25abs ↗pdf ↗

Projection-based multifidelity linear regression for data-scarce applicationsstat.ML

New methods combine low and high-fidelity data for accurate surrogate modeling.

problem Challenges in surrogate modeling for high-dimensional outputs with limited training data.

method Projection-based multifidelity linear regression methods integrating low-fidelity and high-fidelity data.

result Multifidelity methods achieve up to 12% improvement in median accuracy compared to single-fidelity methods.

surrogate modeling multifidelity linear regression data scarcity hypersonic vehicle

2025-08-11abs ↗pdf ↗

Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operatorsstat.ML

Hybrid approach combines VI and HMC for efficient Bayesian inference in neural networks.

problem Computational demands and inaccuracies in Bayesian inference for neural networks.

method Combines VI and HMC, reducing parameter space and accelerating inference.

result Significantly reduces inference time for large neural networks, improving uncertainty quantification.

Hamiltonian Monte Carlo Bayesian inference Neural networks Uncertainty quantification Dimensionality reduction

2025-07-19abs ↗pdf ↗

Mutual Information for Explainable Deep Learning of Multiscale Systemscs.LG

Develops a method to explain deep learning models for complex systems.

problem Rapid simulation-based prototyping of complex systems with high-dimensional CVs and QoIs.

method Moment-independent global sensitivity analysis using differential mutual information.

result Surrogate model driven by mutual information provides useful rankings and optimizations.

mutual information global sensitivity analysis deep learning sensitivity ranking uncertainty quantification

2020-09-07abs ↗pdf ↗

AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kineticscs.LG

AMORE uses neural operators to efficiently predict multiple thermochemical states in stiff chemical kinetics.

problem Efficiently integrating stiff chemical kinetics systems to reduce computational cost.

method Developed AMORE, a framework of adaptive multi-output operator network with two adaptive loss functions.

result Demonstrated improved accuracy and efficiency in predicting thermochemical states from initial conditions.

stiff chemical kinetics neural operators DeepONet adaptive loss functions thermochemical states

2025-10-15abs ↗pdf ↗